GLOBAL CONVERGENCE OF TWO KINDS OF THREE-TERM CONJUGATE GRADIENT METHODS WITHOUT LINE SEARCH

Liang Yin () and Xiongda Chen ()
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Liang Yin: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Hanes Hall, Chapel Hill, NC 27599-3260, USA
Xiongda Chen: Department of Mathematics, Tongji University, Siping Road, Shanghai 200092, China

Asia-Pacific Journal of Operational Research (APJOR), 2013, vol. 30, issue 01, 1-10

Abstract: The conjugate gradient method is widely used in unconstrained optimization, especially for large-scale problems. Recently, Zhang et al. proposed a three-term PRP method (TTPRP) and a three-term HS method (TTHS), both of which can produce sufficient descent conditions. In this paper, the global convergence of the TTPRP and TTHS methods is studied, in which the line search procedure is replaced by a fixed formula of stepsize. This character is of significance when the line search is expensive in some particular applications. In addition, relevant computational results are also presented.

Keywords: Unconstrained optimization; three-term conjugate gradient methods; global convergence (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0217595912500431

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